Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1AIN411Introduction to Bioinformatics3+0+03612.08.2025

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program ARTIFICIAL INTELLIGENCE ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course The objective of this course is to provide students with the information about significant problems in the life-sciences domain and the artificial intelligence applications that are proposed as solutions to these problems at the introductory level, and yielding the necessary theoretical and applicative foundation that constitute the field of bioinformatics.
Course Content Foundations of molecular biology and genetics and the structure and properties of data produced at various levels, DNA and protein sequence analysis, protein 3-D structures and analysis, annotation and prediction of gene and protein functions, phylogenomics and population genetics, gene expression analysis, systems biology and biological networks. Also, AI-based application examples for all of the topics listed above.
Course Methods and Techniques Lecture, discussion, question and answer, preparing and/or presenting reports, problem solving
Prerequisites and co-requisities ( BBM102 ) and ( BBM104 )
Course Coordinator None
Name of Lecturers Associate Prof.Dr. Tunca Doğan
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Zvelebil, Marketa J., and Jeremy O. Baum. Understanding Bioinformatics. Garland Science, 2007. ISBN: 9780815340249. Durbin, Richard, Sean R. Eddy, et al. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, 1998. ISBN: 9780521629713.
Course Notes Zvelebil, Marketa J., and Jeremy O. Baum. Understanding Bioinformatics. Garland Science, 2007. ISBN: 9780815340249.

Durbin, Richard, Sean R. Eddy, et al. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, 1998. ISBN: 9780521629713.


Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Midterm Exam 1 % 25
Assignment 3 % 30
Attendance 1 % 10
Final examination 1 % 35
Total
6
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 3 42
Assignments 3 8 24
Preparation for Midterm Exam 1 30 30
General Exam Preparation 1 40 40
Total Work Load   Number of ECTS Credits 5,93333333333333 178

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 In completion of this course the students will: 1- have an understanding about the terminology used in bioinformatics and computational biology 2- gain a sense of the AI-based theories and application procedures that are employed to answer life-sciences
2  
3  
4  
5  
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to the basic concepts of molecular biology and genetics
2 The structure, properties and diversity of data in bioinformatics and computational biology
3 Biological sequence analysis I - Pairwise sequence alignment
4 Biological sequence analysis II - Multiple sequence alignment
5 Biological sequence analysis III - Similarity/pattern search: profiles & hidden Markov models
6 Biological sequence analysis IV - Sequence alignment algorithms/tools & applications
7 Midterm
8 Proteins structures, analysis & AI applications
9 Biological ontologies, analysis methods & AI applications
10 Gene and protein function annotations and analysis I
11 AI applications in gene and protein function annotations and analysis
12 Gene expression data, analysis & AI applications
13 Phylogenetic trees and their analysis
14 Introduction to systems biology
15 Biological networks & AI applications
16 Final exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 5 5 2 4 5 5 4 4 3 4 2 5
C1
C2
C3
C4
C5
C6
C7
C8

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
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